Scenario-based evaluation of large language models for reference accuracy in dermatology: literature retrieval on latent tuberculosis in psoriasis patients on anti-IL-17/23 therapy

View at Publisher

Ask AI

Bookmark

Share

Key Points

This research aims to assess the accuracy of large language models in retrieving dermatological literature related to latent tuberculosis in patients undergoing specific therapies.
Evaluated LLM performance on reference accuracy in dermatology literature retrieval.
Focused on scenarios involving latent tuberculosis in psoriasis patients on anti-IL-17/23 therapy.
Identified risk of hallucination and the need for verification.
LLM performance showed significant variability with high risk of inaccuracies or 'hallucinations'.
Findings stress the importance of independent verification for reliable outcomes.
Recommendations include testing advanced query techniques and hybrid systems for improved retrieval.

Evaluation shows the accuracy of large language models in dermatology, highlighting verification needs.

No abstract yet. The journal hasn't published one we can show.

No takes yet. Share an insight, caveat, or question.

Altunisik et al. (2026) studied this question.

View at Publisher

Ask AI

Bookmark

Share